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检索条件"任意字段=19th International Conference on Software Engineering and Knowledge Engineering"

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

引用

19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

this study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. the research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. the model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. the attack type of each timestamp were visualized using distinct colors to the grid images. these images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. this graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. this research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

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Energy Efficiency in Sustainable software Development: Clean Code Approaches 4

Energy Efficiency in Sustainable Software Development: Clean...

引用

4th international conference on Electrical engineering and Computer Science, ICECOS 2024

作者： Inayatullah Widhiarso, Wijang Stiawan, Deris Universitas Multi Data Palembang Faculty of Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Department Program in Engineering Science Palembang Indonesia Universitas Multi Data Palembang Faculty of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty Af Computer Science Palembang Indonesia

ISBN: (纸本)9798350368253

the use of technology and information devices contributes to global warming. this issue has also become a concern for UN institutions, as stated in international environmental agreements, which aim to stabilize greenhouse gas emissions in the atmosphere at a level that prevents anthropogenic and dangerous disturbances to the environmental system. this research uses a line of code derived from the tanjak dataset, a student project from 2003 to 2020, numbering 500. this research aims to apply energy consumption efficiency to reduce CO2 emissions produced using computer devices, especially in developing software. the approach used in this research is clean code, which can provide benefits in the form of a line of code that is easy to read, understand, test, and maintain so that it can create savings in energy use. In the test, the line of code used was 1,072,089, producing an energy of 82,152.18 Joules, minus the line of code comments, which was 76,750, producing an energy of 2,971.25 Joules. Initial research results show that the clean code approach can be used to save energy usage, with the result of the line of code used being 995,339 and producing 79,180 Joules of energy, so it can affect reducing CO2 emissions when implementing software development. © 2024 IEEE.

关键词： Greenhouse gas emissions

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35th international conference on software engineering and knowledge engineering, SEKE 2023

作者： Huang, Shizhao Wu, Yimin Lu, Jinwei Deng, Chao School of Software Engineering South China University of Technology Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China School of Computer Science and Engineering Guangdong Ocean University at yangjiang Yangjiang China

Stack Overflow is a widely-used community Q&A website for programming-related queries. In such a platform, providing related questions as suggestions to the users can significantly enhance their search experience. Although there are many approaches based on deep learning that can automatically predict the relatedness between questions, those approaches are limited because the semantic and interaction features of the sentences may be lost. In this paper, we propose a novel method to predict the relatedness between questions based on semantic matching. We adopt the Interaction Feature Extractor to capture the interaction information and fuse it through a fusion mechanism to enhance the interaction between questions. Our experimental results demonstrate that our proposed method achieves state-of-the-art performance in terms of Precision, Recall, and F1-score evaluation metrics, outperforming the baseline approaches. Furthermore, we show that our model also performs well in other semantic matching tasks in software fields, indicating its generalization ability and robustness. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Semantics

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Editorial to the Second IFIP WG 2.7/13.4 Workshop on HCI engineering Education 19th

Editorial to the Second IFIP WG 2.7/13.4 Workshop on HCI Eng...

引用

19th international-Federation-for-Information-Processing-Technical-Committee-13 (IFIP TC13) international conference on Human-Computer Interaction (INTERACT)

作者： Spano, Lucio Davide Campos, Jose Creissac Dittmar, Anke Univ Cagliari Cagliari Italy Univ Minho Braga Portugal HASLab INESC TEC Braga Portugal Braga Portugal Univ Rostock Rostock Germany

ISBN: (纸本)9783031616877;9783031616884

the second workshop on HCI engineering Education continued the effort of the IFIP Working Group 2.7/13.4 on User Interface engineering by discussing the issues and identifying the opportunities in teaching and learning Human-Computer Interaction (HCI) engineering. the workshop attracted eight papers covering different teaching contexts, ranging from massive university courses, passing through different teaching experiences in specific academic curricula, and even teaching engineering concepts to children. In addition, the workshop received input for improving and adapting the repository material to the dynamic nature of this field. the discussion after the presentation of the contributions focused on how to model competencies, the support to interdisciplinary work, the overall course design, the recruitment of the students and the provision of educational resources, paving the way for further editions of the workshop.

关键词： HCI engineering education teaching case study repository Git end-user development model-based design of user interfaces

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Integrating DGBL and Collaborative Learning in Enterprise Resource Planning Courses for Students with engineering Background 26th

Integrating DGBL and Collaborative Learning in Enterprise Re...

引用

26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： May, Ming-Der Lunghwa Univ Sci & Technol Dept Ind Management Taoyuan Taiwan

ISBN: (纸本)9783031519789;9783031519796

Although the current ERP (Enterprise Resource Planning) course has the latest information system software as in the industry that can be operated in class, and with the refinement of PBL and teaching methods, students can learn from the perspective of system implementation, rather than the traditional system operation level. However, for most students, they are still more inclined to work data input, and cannot connect to process-oriented knowledge, or even processes analysis, causing students to fall into repetitive and monotonous system operations. therefore, this study intends to apply the digital online game competition in the ERP courses of the technical University students. To evaluate the "game-based teaching plan and course design strategy", a statistical comparative analysis is carried out on the impact of the ERP course plan. It is hoped that through such an instructional design integrating PBL and game-based learning, it can encourage students' spontaneous and interesting learning motivation and improve the effect of learning participation.

关键词： Digital game-based-learning Online business game Enterprise resource planning

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An Educational Platform for Testing and Evaluating Satellite Control Algorithms in a Real-Time and Frictionless Environment 19

An Educational Platform for Testing and Evaluating Satellite...

引用

19th international Bhurban conference on Applied Sciences and Technology, IBCAST 2022

作者： Ahmad, Khubaib Redah, Atheel Arif, Arslan Saleem, Muhammad Kamran Ahmad Khan, Awais Montenegro, Sergio Faisal, Muhammad University of Central Punjab Electrical Engineering Department Lahore Pakistan University of Wuerzburg Aerospace Information Technology Department Wuerzburg Germany MAchenical Engineering Department University of Engineering and Technology Lahore Pakistan

ISBN: (数字)9781665460514

ISBN: (纸本)9781665460514

Floating satellite (FloatSat) platform for testing and evaluation of Pico/Nano satellite in nearly a frictionless environment. In this paper, we have presented its basic setup and applications regarding communication and control of its speed and velocity. this proposed educational hardware setup serves as a benchmark for students to learn basic satellite systems/subsystems. Furthermore, the setup can be utilized to test and evaluate control algorithms for various space missions. © 2022 IEEE.

关键词： Satellites Space missions software algorithms Benchmark testing Hardware software Real-time systems

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Cooking Gas Monitoring And Alert System 19

Cooking Gas Monitoring And Alert System

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Ab Aziz, Nur Farahwahida binti Rawi, Roziyani binti Mohd Ralim, Nadilah binti Munisamy, Sri Banu Halim, Ridzuan bin Computer Engineering Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9798331507817

Liquid Petroleum Gas (LPG) is widely used in households and the food and beverage industry as an efficient and cost-effective fuel. However, users often struggle to monitor the remaining gas in cylinders, leading to unnecessary expenses from purchasing backup cylinders. Additionally, there is a growing concern about fire hazards due to gas leaks. therefore, developing an IoT-based LPG monitoring and smart alarm system to monitor the remaining gas in the cylinder and notify users when there is gas leakage is essential. the integration of the MQ-6 gas sensor, load cell, HX711, and NodeMCU enables real-time data on the gas level and gas leakage to be sent wirelessly to the mobile application using Wi-Fi technology. Using the pre-installed mobile application, users are able receive real-time updates on the gas level and notification when the gas leakage is detected. the system effectively improves household safety by providing timely notifications and reducing the need for backup cylinders, thus preventing unnecessary expenses. this study facilitates the development of a practical solution that improves household safety and user convenience, concerning both economic and safety aspects. © 2025 IEEE.

关键词： Fire hazards

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Mechanical Fault Prediction Based on Event knowledge Graph 15

Mechanical Fault Prediction Based on Event Knowledge Graph

引用

15th international conference on knowledge engineering and Ontology Development, KEOD 2023 as part of the 15th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2023

作者： He, Li Zhang, Liang Yan, Wei School of Information Science and Engineering Shandong Normal University Shandong Jinan250358 China Shandong University Shandong Qingdao266237 China

ISBN: (纸本)9789897586712

Currently, the majority of diagnoses in the field of mechanical faults are performed by experts or expert systems, which require domain experts to guide the completion while having subpar and limited portability. Consequently, we analyzed the current situation of rolling bearing fault, event knowledge graph and convolutional neural network, explored the intelligent fault diagnosis and prediction technology of rolling bearing, introduced event knowledge graph and convolutional neural network in rolling bearing fault diagnosis, provided support for fault diagnosis and prediction, enhanced the accuracy of fault diagnosis and prediction, and enabled the predictive of complex mechanical equations. Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

关键词： Failure analysis

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Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion 38

Maat: Performance Metric Anomaly Anticipation for Cloud Serv...

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38th IEEE/ACM international conference on Automated software engineering (ASE)

作者： Lee, Cheryl Yang, Tianyi Chen, Zhuangbin Su, Yuxin Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Software Engn Zhuhai Peoples R China

ISBN: (纸本)9798350329964

Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propagate and escalate into failures, making faster-than-real-time anomaly detection highly desirable for expediting downstream analysis and intervention. this paper proposes Maat, the first work to address anomaly anticipation of performance metrics in cloud services. Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting of metric forecasting and anomaly detection on forecasts. the metric forecasting stage employs a conditional denoising diffusion model to enable multi-step forecasting in an auto-regressive manner. the detection stage extracts anomaly-indicating features based on domain knowledge and applies isolation forest with incremental learning to detect upcoming anomalies. thus, our method can uncover anomalies that better conform to human expertise. Evaluation on three publicly available datasets demonstrates that Maat can anticipate anomalies faster than real-time comparatively or more effectively compared with state-of-the-art real-time anomaly detectors. We also present cases highlighting Maat's success in forecasting abnormal metrics and discovering anomalies.

关键词： Could Computing Anomaly Anticipation Denosing Diffusion Model Performance Metric

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AI/ML-Based Water Quality Monitoring Mobile App for Predicting *** in Surface Waters 19

AI/ML-Based Water Quality Monitoring Mobile App for Predicti...

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19th international conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Karumanchi, Keerthi Priya Mhatre, Jui Lee, Ahyoung Lee, Hoseon Department of Computer Science Kennesaw State University MariettaGA30060 United States Department of Electrical and Computer Engineering Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9798331507817

*** contamination in surface waters has proven to be a significant public health concern, requiring innovative monitoring solutions. this paper presents the design of an AI-driven mobile application to predict whether *** bacteria are present at levels exceeding acceptable thresholds in surface waters. the methodology employs sensor devices to collect water quality data parameters, such as water temperature, pH, dissolved oxygen, and turbidity. A dataset is generated based on these parameters, and machine learning (ML) algorithms are applied to evaluate accuracy, precision, recall, and processing time. Additionally, our ML algorithms establish a correlation matrix among water quality parameters to identify the key factors influencing *** levels. We applied various machine learning techniques to the dataset, including Support Vector Regression (SVR), Random Forest Classification (RFC), XGBoost, and ensemble methods that combine these algorithms. Our findings indicate that the ensemble of Random Forest Classification and XGBoost achieved the highest accuracy at 99.9%. Our Mobile App lets Users view E. coli predictions based on current sensor values. © 2025 IEEE.

关键词： Random forests

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